AI-Driven Intrusion Detection A Comprehensive Guide to Securing Distributed Systems

Description

This book presents a framework for implementing next-generation security systems in today’s complex computing landscapes. As organizations migrate to distributed cloud architectures, traditional security approaches fall short against sophisticated threats. This book bridges the gap between theoretical concepts and practical implementation.
Written for security professionals, cloud architects, and AI practitioners, this volume provides both foundational knowledge and advanced techniques—from distributed systems principles to machine learning applications in intrusion detection.
Readers will gain insights into data requirements, feature extraction, and model selection for security applications. The book offers guidance on implementing neural networks and recurrent architectures for cloud security, with emphasis on real-time detection through edge computing.
The text addresses operational challenges including false positive management, adversarial attack mitigation, and explainability in AI security systems. Final chapters explore emerging directions like federated learning and quantum computing applications.
Throughout, the authors emphasize ethical considerations and continuous learning in defensive systems. With its balanced treatment of theory and implementation guidance, this book serves as an essential resource for organizations enhancing security posture through AI-driven detection in distributed cloud environments.